ORIGINAL

Abstract

Risk adjustment under the Affordable Care Act (ACA) is a program where insurers with sicker-than-average enrollees receive risk transfer payments from insurers with healthier-than-average enrollees. Our goal is to understand the elements driving risk transfers. First, the distribution of risk transfers should be based on random health shocks, which are unpredictable events that negatively affect health status. Second, risk transfers could be influenced by factors unique to each insurer, such as certain plans attracting certain patients, the extent to which carriers engage in risk selection, and the degree of upcoding. We hypothesize that random health shocks driving risk transfers can be represented by the sum of normal random variables with some variance in patient sickness that is universal across the country up to a cost-adjustment factor. We attempt to fit empirical risk transfer data from the 2014-2017 benefit years to this purely normal model. We find that no possible value of variance in patient sickness allows this model to remain normally distributed while accounting for outliers outside 2 standard deviations. We then lower bound the proportion of transfers that cannot be accounted for by adding randomly distributed health shocks. We find that over all states included in our dataset, at least 60% of the volume of transfers cannot be accounted for by a purely normal model. Our results suggest the existence of a non-normal component of risk transfers that reflects factors unique to each insurer. Further investigation is needed to find the relative importance of different insurer-specific factors in explaining overall risk-adjustment transfers.